Application of Top-hat Transformation for Enhanced Blood Vessel Extraction

TL;DR

This paper presents a novel blood vessel extraction method combining top-hat preprocessing with a B-COSFIRE filter, improving accuracy and reducing false positives in retinal images for better diagnosis of vascular diseases.

Contribution

It introduces a new approach integrating top-hat transformation with B-COSFIRE filtering, enhancing vessel segmentation accuracy in challenging retinal images.

Findings

Improved vessel extraction accuracy over existing methods.

Effective reduction of false positives in vessel segmentation.

Enhanced robustness in presence of retinal structures like exudates and hemorrhages.

Abstract

In the medical domain, different computer-aided diagnosis systems have been proposed to extract blood vessels from retinal fundus images for the clinical treatment of vascular diseases. Accurate extraction of blood vessels from the fundus images using a computer-generated method can help the clinician to produce timely and accurate reports for the patient suffering from these diseases. In this article, we integrate top-hat based preprocessing approach with fine-tuned B-COSFIRE filter to achieve more accurate segregation of blood vessel pixels from the background. The use of top-hat transformation in the preprocessing stage enhances the efficacy of the algorithm to extract blood vessels in presence of structures like fovea, exudates, haemorrhages, etc. Furthermore, to reduce the false positives, small clusters of blood vessel pixels are removed in the postprocessing stage. Further, we find that the proposed algorithm is more efficient as compared to various modern algorithms reported in the literature.